U.S. patent application number 16/682909 was filed with the patent office on 2020-05-14 for movement reconstruction control system.
The applicant listed for this patent is GTX medical B.V.. Invention is credited to Jurriaan BAKKER, Niek BORGERS, Miroslav CABAN, Vincent DELATTRE, Urs KELLER, Emmanuel PIGNAT, Joachim von ZITZEWITZ.
Application Number | 20200152078 16/682909 |
Document ID | / |
Family ID | 64308559 |
Filed Date | 2020-05-14 |
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United States Patent
Application |
20200152078 |
Kind Code |
A1 |
CABAN; Miroslav ; et
al. |
May 14, 2020 |
MOVEMENT RECONSTRUCTION CONTROL SYSTEM
Abstract
The present invention relates to a control system for a movement
reconstruction and/or restoration system for a patient, comprising
a movement model generation module to generate movement model data
information, an analysis module receiving and processing data
provided at least by the movement model generation module, wherein
the control system is configured and arranged to prepare and
provide on the basis of data received by the movement model
generation module and the analysis module a movement model
describing the movement of a patient and providing, on the basis of
the movement model, stimulation data for movement reconstruction
and/or restoration.
Inventors: |
CABAN; Miroslav; (Eindhoven,
NL) ; BORGERS; Niek; (Eindhoven, NL) ; KELLER;
Urs; (Eindhoven, NL) ; von ZITZEWITZ; Joachim;
(Eindhoven, NL) ; BAKKER; Jurriaan; (Eindhoven,
NL) ; DELATTRE; Vincent; (Eindhoven, NL) ;
PIGNAT; Emmanuel; (Eindhoven, NL) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
GTX medical B.V. |
Eindhoven |
|
NL |
|
|
Family ID: |
64308559 |
Appl. No.: |
16/682909 |
Filed: |
November 13, 2019 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
A61N 1/36128 20130101;
A61N 1/36003 20130101; G05B 13/042 20130101; G16H 50/50 20180101;
A61N 1/36062 20170801; G05B 13/028 20130101; G09B 19/003
20130101 |
International
Class: |
G09B 19/00 20060101
G09B019/00; G05B 13/02 20060101 G05B013/02; G05B 13/04 20060101
G05B013/04 |
Foreign Application Data
Date |
Code |
Application Number |
Nov 13, 2018 |
EP |
18205814.9 |
Claims
1. A control system for a movement reconstruction and/or
restoration system for a patient, comprising a movement model
generation module to generate movement model data information; and
an analysis module receiving and processing data provided at least
by the movement model generation module; wherein the control system
is configured and arranged to prepare and provide on the basis of
data received by the movement model generation module and the
analysis module a movement model describing a movement of a patient
and providing, on the basis of the movement model, stimulation data
for movement reconstruction and/or restoration.
2. The control system according to claim 1, wherein the analysis
module is a real-time analysis module.
3. The control system according to claim 2, wherein the control
system further comprises an input module for receiving sensor input
data, the sensor input data describing a phase of the movement.
4. The control system of claim 3, wherein the control system
further comprises a preprocessing module for preprocessing sensor
input data received by the input module.
5. The control system of claim 3, wherein the control system
further comprises a filter to filter the data provided by the
real-time analysis module.
6. The control system of claim 5, wherein the filter is at least
one of a Kalman filter, a histogram filter, a particle filter, or a
stochastic filter.
7. The control system of claim 3, wherein the movement model
generation module is configured and arranged to prepare and provide
the movement model, wherein the movement model generation module is
a learning system.
8. The control system of claim 7, wherein the control system is
configured and arranged to prepare and provide the movement model
on the basis of a fusion of sensor input data and movement
model.
9. The control system of claim 3, wherein the control system
comprises at least one sensor data buffer.
10. The control system of claim 3, wherein the control system
comprises at least one offline expert system.
11. The control system of claim 3, wherein the control system
comprises at least one movement event library.
12. The control system of claim 3, wherein the control system
comprises at least one model training module.
13. The control system of claim 3, wherein the control system is
configured and arranged to extract at least one base frequency out
of sensor input data, wherein the base frequency is indicative of a
cadence of the movement.
14. The control system of claim 3, wherein the control system
comprises a pre-warning module, which is configured and arranged to
provide a pre-warning signal indicative of providing an upcoming
stimulation event.
Description
CROSS-REFERENCE TO RELATED APPLICATION
[0001] The present application claims priority to European Patent
Application No. 18205814.9, filed on Nov. 13, 2018. The entire
contents of the above-listed application is hereby incorporated by
reference for all purposes.
TECHNICAL FIELD
[0002] The present invention relates to a system for controlling a
movement reconstruction and/or restoration system for a patient,
e.g. in the field of improving recovery after neurological
disorders like spinal cord injury (SCI), for example after
trauma.
BACKGROUND AND SUMMARY
[0003] Decades of research in physiology have demonstrated that the
mammalian spinal cord embeds sensorimotor circuits that produce
movement primitives (cf Bizzi E. et al., Modular organization of
motor behavior in the frog's spinal cord. Trends in neurosciences
18, 442-446 (1995); Levine A J. et al., Identification of a
cellular node for motor control pathways. Nature neuroscience 17,
586-593 (2014)). These circuits process sensory information arising
from the moving limbs and descending inputs originating from
various brain regions in order to produce adaptive motor
behaviors.
[0004] A spinal cord injury (SCI) interrupts the communication
between the spinal cord and supraspinal centers, depriving these
sensorimotor circuits from the excitatory and modulatory drives
necessary to produce movement.
[0005] A series of studies in animal models and humans showed that
electrical neuromodulation of the lumbar spinal cord using epidural
electrical stimulation (EES) is capable of (re-)activating these
circuits. For example, EES has restored coordinated locomotion in
animal models of SCI, and isolated leg movements in individuals
with motor paralysis (cf van den Brand R. et al., Restoring
Voluntary Control of Locomotion after Paralyzing Spinal Cord
Injury. Science 336, 1182-1185 (2012); Angeli C A. et al., Altering
spinal cord excitability enables voluntary movements after chronic
complete paralysis in humans. Brain: a journal of neurology 137,
1394-1409 (2014); Harkema S. et al., Effect of epidural stimulation
of the lumbosacral spinal cord on voluntary movement, standing, and
assisted stepping after motor complete paraplegia: a case study.
The Lancet 377, 1938-1947 (2011); Danner S M et al., Human spinal
locomotor control is based on flexibly organized burst generators.
Brain: a journal of neurology 138, 577-588 (2015); Courtine G. et
al., Transformation of nonfunctional spinal circuits into
functional states after the loss of brain input. Nature
neuroscience 12, 1333-1342, (2009); Capogrosso M et al., A
brain-spine interface alleviating gait deficits after spinal cord
injury in primates. Nature 539, 284-288, (2016)).
[0006] EP 2 868 343 A1 discloses a system to deliver adaptive
electrical spinal cord stimulation to facilitate and restore
locomotion after neuromotor impairment. Inter alia, a closed-loop
system for real-time control of epidural electrical stimulation is
disclosed, the system comprising means for applying to a subject
neuromodulation with adjustable stimulation parameters, said means
being operatively connected with a real-time monitoring component
comprising sensors continuously acquiring feedback signals from
said subject. The feedback signals provide features of motion of a
subject, wherein the real-time monitoring component is operatively
connected with a signal processing device receiving feedback
signals and operating real-time automatic control algorithms. This
known system improves consistency of walking in a subject with a
neuromotor impairment. A Real-Time Automatic Control Algorithm is
used, comprising a feedforward component employing a single
input-single output model (SISO), or a multiple input-single output
(MISO) model. Reference is also made to Wenger N. et al.
Closed-loop neuromodulation of spinal sensorimotor circuits
controls refined locomotion after complete spinal cord injury,
Science Translational Medicine, 6, 255 (2014).
[0007] WO 2002/034331 A2 discloses a non-closed loop implantable
medical device system that includes an implantable medical device,
along with a transceiver device that exchanges data with the
patient, between the patient and the implantable medical device,
and between a remote location and the implantable medical device. A
communication device coupled to the transceiver device exchanges
data with the transceiver device, the implantable medical device
through the receiver device, and between the transceiver device and
the remote location to enable bi-directional data transfer between
the patient, the implantable medical device, the transceiver
device, and the remote location. A converter unit converts
transmission of the data from a first telemetry format to a second
telemetry format, and a user interface enables information to be
exchanged between the transceiver device and the patient, between
the implantable medical device and the patient through the
transceiver device, and between the patient and the remote location
through the transceiver device.
[0008] EP 3 184 145 A1 discloses systems for selective
spatiotemporal electrical neurostimulation of the spinal cord. A
signal processing device receiving signals from a subject and
operating signal-processing algorithms to elaborate stimulation
parameter settings is operatively connected with an Implantable
Pulse Generator (IPG) receiving stimulation parameter settings from
said signal processing device and able to simultaneously deliver
independent current or voltage pulses to one or more multiple
electrode arrays. The electrode arrays are operatively connected
with one or more multi-electrode arrays suitable to cover at least
a portion of the spinal cord of said subject for applying a
selective spatiotemporal stimulation of the spinal circuits and/or
dorsal roots, wherein the IPG is operatively connected with one or
more multi-electrode arrays to provide a multipolar stimulation.
Such system advantageously allows achieving effective control of
locomotor functions in a subject in need thereof by stimulating the
spinal cord, in particular the dorsal roots, with spatiotemporal
selectivity.
[0009] EP 2 652 676 A1 relates to a gesture control for monitoring
vital body signs and reuses an accelerometer, or, more precise,
sensed accelerations of a body sensor for user control of the body
sensor. This is achieved by detecting predefined patterns in the
acceleration signals that are unrelated to other movements of the
patient. These include tapping on/with the sensor, shaking, and
turning the sensor. New procedures are described that make it
possible to re-use the acceleration sensing for reliable gesture
detection without introducing many false positives due to
non-gesture movements like respiration, heartbeat, walking, etc.
Similar solutions for tapping detection of a user are known from
U.S. Pat. Nos. 8,326,569 and 7,742,037.
[0010] WO 2017/062508 A1 discloses a system for controlling a
therapeutic device and/or environmental parameters including one or
more body worn sensor devices that detect and report one or more
physical, physiological, or biological parameters of a person in an
environment. The sensor devices can communicate sensor data
indicative of the one or more physical, physiological, or
biological parameters of a person to an external hub that processes
the data and communicates with the therapeutic device to provide a
therapy (e.g., neuromodulation, neurostimulation, or drug delivery)
as a function of the sensor data. In some embodiments, the
therapeutic device can be implanted in the person. In some
embodiments, the therapeutic device can be in contact with the skin
of the person. The sensor devices can also communicate to the hub
that communicates with one or more devices to change the
environment as a function of the sensor data.
[0011] WO 2010/021977 describes an orthotic apparatus for use in
providing improved range of motion which allows the amount of
stretch to be hydraulically powered and measured by the device but
controlled by the user. Because the apparatus accurately calculates
the amount of stretch, the user, together with the user's physician
and therapist, can develop a rehabilitation plan based on accurate
measurements. Progress is based on tangible results rather than the
user's ability to tolerate pain.
[0012] EP 2 966 422 A1 describes a method for measuring parameters,
such as human weight in motion. The method provides registration of
signals generated by load sensors disposed in shoe insoles. Each
insole has at least two load sensors, with one mounted near the
heel region and the other near the toe region of foot. The specific
type of motor activity is determined based on temporal correlation
of the load sensor signals from both insoles and values thereof.
The person's weight, including additionally carried weight, is
determined by summing up load sensor signals, for a specific type
of motor activity. The invention provides for the measurement of
person's weight, including additionally carried weight, in
real-time for different types of motor activity, such as running,
walking at different pace, standing.
[0013] WO 2017/058913 relates to systems and methods to analyze
gait, balance or posture information extracted from data collected
by one or more wearable and connected sensor devices with sensors
embedded there within. The embedded sensors include a three-axis
accelerometer, a three-axis gyroscope and an array of pressure
sensors. Sensor data detected by the sensors can be received by a
mobile computing device, which can analyze the sensor data to
identify a pattern related to gait, balance or posture within the
sensor data; and apply a statistical/machine learning-based
classification to the pattern related to gait, balance or posture
to assign a clinical parameter to the pattern characterizing a risk
of a slip, trip and fall event.
[0014] US 2015/196231 discloses a method for acquiring gait
parameters of an individual. The method includes capturing
calibration images from foot marker placed on feet or shoes of an
individual while an individual is standing still, the calibration
images are obtained from a camera worn by the individual, capturing
subsequent time-varying images from the foot markers while the
individual is walking, and comparing the calibration images to the
subsequent time-varying images by a processing unit that is coupled
to the camera to determine changes between the initial relative
image size of the foot markers and the time-varying images of the
foot markers as a function of time to analyze gait of the
individual.
[0015] According to the state of the art, smooth movements
comparable to healthy subjects still cannot be achieved by the
subject. There is a desire to have a system which overcomes the
drawbacks of the prior art. In particular, there is the need of a
system stimulating the patient not as a robot but to stimulate the
patient's control loops within their body. A good roll of the foot
and no parasite movements are necessary during walking. Thus, the
goal of applying stimulation is not to control the patient as a
robot, but to support the patient during training and daily life
activities, e.g. walking cadence. Hence, a control system should be
able to determine a movement event, e.g. a gait event with criteria
that are common to all kind of healthy or pathological movement,
e.g. gait, and should support the patient's own natural control
loop composed of the brain, nervous system, and sensory organs.
[0016] It is an object of the present invention to improve a
neurostimulation system, e.g. in the field of improving recovery
after neurological disorders like spinal cord injury (SCI), for
example after trauma, especially in adding a control system for a
movement reconstruction and/or restoration system for a
patient.
[0017] This object is solved according to the present invention by
a control system for a movement reconstruction and/or restoration
system for a patient, with the features of claim 1. Accordingly, a
control system for a movement reconstruction and/or restoration
system for a patient is provided, comprising
[0018] a movement model generation module to generate movement
model data information;
[0019] an analysis module receiving and processing data provided at
least by the movement model generation module;
[0020] wherein the control system is configured and arranged to
prepare and provide on the basis of data received by the movement
model generation module and the analysis module a movement model
describing the movement of a patient and providing, on the basis of
the movement model, stimulation data for movement reconstruction
and/or restoration.
[0021] The invention is based on the basic idea that in the context
of neuromodulation, especially neurostimulation, the electrical
stimulation parameters defining the stimulation in a movement
reconstruction and/or restoration system for a patient can be
controlled with said system, wherein a movement model is prepared
and provided on the basis of model data. Such model data may be
based on inter alia recent sensor data of the movement of the
patient and a previous movement model, and on the basis of the
movement model, stimulation of data for movement reconstruction
and/or restoration is provided. Movement kinematics are calculated
using rigorous mathematical protocols and movement abnormalities
are identified. The control system may interfere with the feedback
loop of the patient, as control inputs are identified that are
required for the control system to produce the correct stimulation.
Altogether, this enables motion, e.g. a gait cycle, with regular
characteristics, comparable to a healthy subject.
[0022] The system can be used for treatment related but not limited
to restoring and or training of the movements of the patient. Such
a movement could be e.g. walking, running, stepping, swimming,
rowing or cycling, or any other cyclic or non-cyclic movement.
[0023] By directly and/or indirectly attaching one or more sensors
to the head and/or the neck and/or the trunk and/or the abdomen
and/or the waist and/or at least one limb and/or part of a limb
and/or foot motion parameters (including but not limited to
position and/or angular velocity and/or angle) of the respective
body parts can be determined during motion, e.g. gait cycle, to
realize the reorganization of the various phases, e.g. gait
phase.
[0024] Neural stimulation may be achieved by electrical
stimulation, optogenetics (optical neural stimulation), chemical
stimulation (implantable drug pump), ultrasound stimulation,
magnetic field stimulation, mechanical stimulation, etc.
[0025] Known electrical stimulation systems use either Central
Nervous System (CNS) stimulation, especially Epidural Electrical
Stimulation (EES), or Peripheral Nervous System (PNS) Stimulation,
especially Functional Electrical Stimulation (FES).
[0026] Epidural Electrical Stimulation (EES) is known to restore
motor control in animal and human models and has more particularly
been shown to restore locomotion after spinal cord injury by
artificially activating the neural networks responsible for
locomotion below the spinal cord lesion (Capogrosso M et al., A
Computational Model for Epidural Electrical Stimulation of Spinal
Sensorimotor Circuits, Journal of Neuroscience, 33 (49),
19326-19340 (2013); Courtine G. et al., Transformation of
nonfunctional spinal circuits into functional states after the loss
of brain input, Nat Neurosci. 12(10), 1333-1342 (2009); Moraud E M.
et al., Mechanisms Underlying the Neuromodulation of Spinal
Circuits for Correcting Gait and Balance Deficits after Spinal Cord
Injury, Neuron, 89(4), 814-828 (2016)). EES does not directly
stimulate motor-neurons but the afferent sensory neurons prior to
entering into the spinal cord. In this way, the spinal networks
responsible for locomotion are recruited indirectly via those
afferents, restoring globally the locomotion movement by activating
the required muscle synergies. The produced movement is functional;
however, due to relatively poor selectivity (network activation
instead of selective targeting of key muscles) the controllability
is low, and the imprecisions hinder fluidity and full functionality
in the potential space of the movement.
[0027] PNS stimulation systems used to date in the clinic are known
as Functional Electrical Stimulation (FES) that provides electrical
stimulation to target muscles with surface electrodes, either
directly through stimulation of their motorfibers (neuro-muscular
stimulation), or through a limited set reflexes (practically
limited to the withdrawal reflex) or by transcutaneously
stimulating the peripheral nerves. The resulting muscle fatigue has
rendered FES unsuitable for use in daily life. Furthermore,
successes have remained limited through cumbersome setups when
using surface muscle stimulation, unmet needs in terms of
selectivity (when using transcutaneous nerve stimulation) and a
lack of stability (impossible to reproduce exact electrode
placement on a daily basis when stimulating muscles, moving
electrodes due to clothes, sweating).
[0028] It is possible to provide neuromodulation and/or
neurostimulation with the system to the CNS and to the PNS. Both
CNS and PNS can be stimulated at the same time or also
intermittently or on demand. These two complementary stimulation
paradigms can be combined into one strategy and made available for
a patient being equipped with the system. For example,
neuromodulation and/or neurostimulation of the CNS may be used to
enhance and/or restore the patient's capabilities of movement,
especially in a way that the existing ways of physiological signal
transfer in the patient's body is supported such that the command
signals for body movement or the like still are provided by the
patient's nervous system and just supported and/or enhanced or
translated by the CNS stimulation module. The stimulation provided
by the PNS module may be used to specifically steer and direct
stimulation signals to specific peripheral nervous structures in
order to trigger a specific movement and/or refine existing
movements. Such a PNS stimulation may be used to refine and/or
complete motion and/or movement capabilities of the patient being
equipped with the system. It can be e.g. used to complete flexion
or extension, lifting, turning or the like of inter alia but not
limited to toes, fingers, arms, feet, legs or any extremities of
the patient. This can be e.g. done in cases where it is realized
that the neuromodulation and/or neurostimulation provided by the
CNS stimulation module is not sufficient to complete a movement or
intended status of the patient. Then, such a movement or intended
status may be completed or supported by stimulation provided by the
PNS stimulation system. The PNS stimulation can be also used to
reduce side effects or compensate for imprecisions of the CNS
stimulation.
[0029] EES can be phasic or tonic, selective PNS is always phasic.
Phasic is defined as locked to defined events in the sensing
signals (decoded intention, continuous decoding, muscle activity
onset, movement onset, event during defined movement (foot off or
foot strike during gait for instance).
[0030] By PNS stimulation, a stimulation of the upper limb nerves,
i.e. the radial, ulnar and/or median nerves can be provided. Also,
the lower limb nerves like the sciatic and/or femoral nerves can be
provided by PNS stimulation. All PNS stimulation can be done by
targeting one of the above-mentioned nerves with intra-neural
electrodes (transversal or longitudinal) or epi-neural (cuff)
electrodes.
[0031] By CNS stimulation the following nervous structures may be
stimulated: for the upper limb movements the cervical spinal cord
or hand/arm motor cortex may be stimulated with the CNS stimulation
module. For the lower limb movements, the lumbosacral spinal cord
may be stimulated. All these nerves can be targeted with epidural,
subdural or intra-spinal/intra-cortical stimulation.
[0032] Both PNS and CNS stimulation modules may be for example but
not limited to implantable pulse generators (IPGs) for a
neuromodulation system or the like.
[0033] IPGs can be used for providing the necessary stimulation
current and signals for the CNS stimulation system and the PNS
stimulation system. The IPG produces the stimulation pulses that
are delivered by a lead with multiple electrodes to the stimulation
side, e.g. spinal cord. For EES, the lead is positioned in the
epidural space (i.e. on the outside of the dural sac, which encases
the spinal cord and the cerebrospinal fluid in which the spinal
cord `floats`), on top of the spinal cord (including but not
limited to the segments T12, L1, L2, L3, L4, L5, and S1
bilaterally).
[0034] It is also possible that two separated IPGs are provided,
one for the PNS stimulation system and one for the CNS stimulation
system.
[0035] The stimulation parameters for the PNS stimulation and the
EES stimulation may be frequency, amplitude, pulse-width and the
like.
[0036] Both CNS and PNS stimulations, as well as the combination of
these stimulation systems may be used in a sub-motor threshold
region, i.e. an amplitude or configuration at which neuronal
sensation but no motor response is evoked.
[0037] The stimulation may be performed in an open-loop manner,
where a pre-defined fixed stimulation is executed without adapting
to e.g. the motion of the patient. The stimulation settings may
then be determined by the therapist or physiotherapist. The
movement of the patient is recorded.
[0038] The stimulation may be performed in a closed-loop manner,
where feedback is used to adjust the stimulation to movement of the
patient, including but not limited to walking or cycling.
[0039] The system may be also applied for a patient being supported
by an external device, including but not limited to an exoskeleton,
body-weight support, a walker, or crutches.
[0040] Said sensor input data are provided by one or more motions
sensors. In particular, it is possible that two or more sensors
form a sensor network.
[0041] Said sensor may comprise at least one of an inertial
measurement unit (IMU), a piezo element, a velocity sensor, an
accelerometer, a magnet sensor, a pressure sensor, a displacement
sensor, a contact sensor, a goniometer, a magnetic field sensor, a
hall sensor and/or a gyroscope and/or motion tracking video camera,
or infra-red camera.
[0042] Some sensors may be worn by the patient without acquiring
fixed base station, including but not limited to piezo elements,
pressure sensors and/or torque sensors.
[0043] Said sensor may be configured and arranged to be inserted
and/or integrated into and/or onto an exoskeleton, tights, a belt,
straps, a stretching band, a knee sock, a sock and/or a shoe of the
patient.
[0044] Said sensor may be intended to be placed on the foot to get
to most information possible about the movement, e.g. gait.
[0045] In particular, two or more sensors placed on one foot and/or
any other suitable position of the body such as another part of a
leg, including but not limited to the shank and/or thigh and/or hip
and/or other parts of the body including but not limited to the
trunk and/or the abdomen and/or one or two arms and/or one or two
hands and/or another part of an arm and/or the head and/or the neck
of the patient may provide a precise description of the cadence,
swing, stance, heel strike, heel lift and toe-off can be
identified. The same events and parameters can be identified for
the other foot and/or another part of a leg, including but not
limited to the shank and/or thigh and/or hip and/or other parts of
the body including but not limited to the trunk and/or the abdomen
and/or one or two arms and/or one or two hands and/or another part
of the arm and/or the head and/or the neck of the patient. By
combining signals of the sensors, together with the movement, e.g.
gait phase and cadence of the stimulation input, a reliable
movement phase, e.g. gait phase and cadence estimate can be
provided.
[0046] Said sensors may be lightweight and wearable, thus the
sensors may not hamper the movement of the patient.
[0047] In particular, said sensor may be placed at different
positions in the shoe or into the shoe sole and/or into the shoe
insole. One shoe and/or one shoe sole and/or one shoe insole may be
equipped with one or more sensors. Said sensors may be placed in
the heel area and/or the metatarsal area and/or the toe area.
[0048] Said IMU may measure and report 3D accelerations, 3D angular
velocities and 3D orientation using a combination of one or more of
an accelerometer, one or more of gyroscopes, and optionally one or
more of a magnetometer. Optionally, a temperature sensor may also
be included to compensate for the effect of temperature on sensor
readings. By integrating the angular velocity assessed by said one
or more gyroscopes and fusing with data from said one or more
accelerometers, it may be possible to get a precise measurement of
the angle of the foot and/or another part of a leg, including but
not limited to the shank and/or thigh and/or hip and/or other parts
of the body including but not limited to the trunk, and/or one or
two arms and/or one or two hands and/or another part of an arm
and/or the head and/or the neck of the patient. This angle may have
a regular and characteristic pattern for a healthy subject but not
for an injured patient. Based on these measurements the orientation
of the IMU with respect to the fixed world can be estimated
accurately, using standard sensor fusion algorithms.
[0049] Body part position such as foot position, shank position,
trunk position, arm position, thigh position, ankle position,
shoulder position, head position or the like estimates can be
obtained by double integration of the measured acceleration in
combination with drift correction. In this way, non-real-time
reconstruction of e.g. foot trajectories (or any other trajectories
of a body part) can be done up to a few centimeters accuracy for
healthy subjects. Any such body position alone or in combination
with other positions and especially the change of such positions
can be used to describe and calculate a movement or movement
phase.
[0050] The control system may comprise a preprocessing module for
preprocessing the sensor input data The preprocessing module may
preprocess the sensor data. In particular, the preprocessing of
data may be based on algorithms that use a series of measurements
observed over time, containing statistical noise and other
inaccuracies, and produce estimates of unknown variables that tend
to be more accurate than those based on a single measurement, by
estimating a joint probability distribution over the variables for
each timeframe.
[0051] The preprocessing module may be configured and arranged to
and/or responsible for shifting, compressing, and normalizing the
raw data into a format that improves the performance of the
subsequent modules
[0052] In particular, such a preprocessing module may be or
comprise a Kalman filter or any other suitable filter.
[0053] Preprocessing may be used especially in connection with
machine learning or self-learning systems and/or algorithms.
[0054] In general, learning algorithms benefit from standardization
of the data set. If some outliers are present in the set, robust
scalers or transformers are more appropriate.
[0055] Standardization of datasets is a common requirement for many
machine learning estimators in machine learning systems and
algorithms; they might behave badly if the individual features do
not more or less look like standard normally distributed data, e.g.
Gaussian with zero mean and unit variance. In practice the shape of
the distribution is often ignored and just transformed the data to
center it by removing the mean value of each feature, then scale it
by dividing non-constant features by their standard deviation. For
instance, many elements used in the objective function of a
learning algorithm (such as the RBF kernel of Support Vector
Machines or the l1 and l2 regularizers (Lasso Regression or Ridge
Regression) of linear models) assume that all features are centered
around zero and have variance in the same order. If a feature has a
variance that is orders of magnitude larger than others, it might
dominate the objective function and make the estimator unable to
learn from other features correctly as expected.
[0056] In particular, by integrating the angular velocity and
fusing with data from an accelerometer and preprocessing these data
with a Kalman filter, it may be possible to get a precise
measurement of the angle of the foot and/or another part of a leg,
including but not limited to the shank and/or thigh and/or hip
and/or other parts of the body including but not limited to the
trunk, and/or one or two arms and/or one or two hands and/or
another part of an arm and/or the head and/or the neck of the
patient. This angle also has a regular and characteristic pattern
for a healthy subject but not for an injured patient.
[0057] The movement model generation module may generate a movement
model. The movement model generation module uses preprocessed
sensor data to prepare a movement model.
[0058] The analysis module may receive, and process data provided
by the preprocessing module and the movement model generation
module.
[0059] In particular, the analysis module may be a real-time
analysis module.
[0060] After the preprocessing step, the current state of the
movement may be used to estimate the probability density function
of the movement phase. The gait speed of the movement phase is
retrieved a single normal distribution to be used in the
filter.
[0061] The control system may further comprise an input module for
receiving sensor input data, the sensor input data describing a
phase of a movement.
[0062] The input module may be embodied as or comprise a Bluetooth
module, which is e.g. placed on the motion controller that receives
and forwards the data.
[0063] Furthermore, there may be a filter to filter the data
provided by the real-time analysis module.
[0064] For example, the use of a general concept including an input
module, a preprocessing module, a movement model generation module,
a real-time analysis module, and a filter and made available for a
movement reconstruction system for a patient being equipped with
the movement reconstruction and/or restoration system enables to
allow triggering neurostimulation based on sensor input data from
the patient and a preexisting movement model.
[0065] In particular, the movement model generation module may be
configured and arranged to prepare and provide the movement model,
wherein the movement model generation module is a learning
system.
[0066] At the beginning of a rehabilitation session, the movement
model used may be a general model. The movement model used at the
beginning of a rehabilitation session may be trained on a set of
different subjects, e.g. one or more trainers, and/or one or more
healthy subjects and/or one or more patients. The movement model
thus may not be perfect for the individual patient but sufficient,
e.g. in the case of a gait model sufficient to make some steps.
Thus, the movement model may be trained by an online learning
system to adapt to the patient's individual movement
kinematics.
[0067] For the online learning system, it may be possible to tune
the learning rate, namely, the rate at which the model adapts to
change in the movement, e.g. gait cycle.
[0068] It may be possible to stop the learning process when the
movement model is good enough and to store it for further sessions
with the same patient.
[0069] Moreover, the control system may be configured and arranged
to prepare and provide the movement model on the basis of a fusion
of sensor input data and movement model.
[0070] In particular, recent sensor data from the one or more
sensors may be fused to the existing movement model to update the
movement model. As soon as a whole movement, e.g. a gait cycle of
the patient is detected, the online learning system may determine
the past movement event and the existing movement model may be
trained to adapt to the new data. The movement model used online
may be updated. As a result, each recently generated movement model
is based on more data than the previous movement model.
[0071] Based on the movement model, the stimulation of the patient
may be provided via one or more IPGs.
[0072] Moreover, the control system may comprise at least one
sensor data buffer.
[0073] In particular, sensor data from one or more sensors and/or
one or more sensor networks may be recorded for at least one
complete movement or movement cycle, e.g. gait cycle, and stored in
a sensor buffer. In particular, the sensor buffer may accumulate
sensor data from one or more sensors and/or one or more sensor
networks.
[0074] Furthermore, the control system may comprise at least one
offline expert system.
[0075] In particular, the offline expert system may process
recorded sensor data for a period of time of minimum one complete
movement, e.g. gait cycle, offline. The gait phase consequently may
be corresponding to the recorded data buffer. In particular, the
gait phase may be created so that it may take always the same value
at the same event.
[0076] In particular, offline analysis may enable to use criteria
that could not be used on real-time.
[0077] We define real-time as an end-to-end latency that is less
than 100 ms, preferably less than 50 ms.
[0078] Moreover, the control system may comprise at least one
movement event library.
[0079] Based on the sensor data for the recorded period of time the
offline expert system determines a movement event library offline
and creates a movement model, e.g. gait model, for the recorded
movement, e.g. gait events.
[0080] Hence, the stimulation may be at any given "time" of a
movement cycle, e.g. gait cycle.
[0081] In particular, the movement event library may comprise
stimulation settings for one complete movement cycle, including but
not limited to a gait cycle.
[0082] In particular, for one gait cycle, the movement event
library at least includes the stimulation settings for the two
events foot-strike and foot-off, but also more events may be
set.
[0083] Said events of the movement event library may be present
amongst all kind of patient.
[0084] Furthermore, the control system may comprise at least one
model training module.
[0085] In particular, the model training module integrates recorded
sensor data and corresponding movement phase, e.g. gait phase. Said
module may train the movement model, e.g. gait model, using recent
data to adapt to the particular movement, e.g. gait of the patient
using a non-linear regression technique.
[0086] In particular, the filter may be at least one of a Kalman
filter, a histogram filter, a particle filter, a stochastic filter
or the like.
[0087] By means of a filtering process, it may be possible to take
into account estimation of previous time steps to get a movement
phase that is consistent in time.
[0088] The general idea behind filtering is to establish a best
estimate for the true value of the movement model from an
incomplete, potentially noisy set of observations on that movement
model and corresponding movement.
[0089] The Kalman filter is the estimation part of the optimal
control solution to the linear-quadratic-Gaussian control problem.
It is an algorithm that uses a series of measurements observed over
time, containing statistical noise and other inaccuracies, and
produces estimates of unknown variables that tend to be more
accurate than those based on a single measurement alone, by
estimating a joint probability distribution over the variables for
each timeframe.
[0090] The main idea behind a Histogram filter is to run through
the signal entry by entry, replacing each entry with the median of
neighboring entries.
[0091] Particle filters are methods of genetic, Monte Carlo
algorithms used to solve filtering problems arising in signal
processing and Bayesian statistical inference. The filtering
problem consists of estimating the internal states in dynamical
systems when partial observations are made, and random
perturbations are present in the sensors as well as in the
dynamical system.
[0092] Moreover, the control system may be configured and arranged
to extract at least one base frequency out of sensor input data,
especially wherein the base frequency is indicative of a cadence of
the movement.
[0093] Each movement, e.g. gait, may vary between two or more
patients, as well as for a single patient for e.g. different
walking speeds and different assistive devices, including but not
limited to body-weight support, walker, crutches. Especially for
impaired gait, not all gait events are always present. However, as
e.g. walking is a periodic motion, all measured signals are also
periodic. Hence, it is always possible to estimate the cadence by
extracting the base frequency of the measured signals.
[0094] The cadence of the left foot should be equal to the cadence
of the right foot and the cadence of the provided stimulation, and
the left foot and right foot should be (roughly) in anti-phase.
Machine-learning methods may be used to adapt the gait phase
estimation to the gait of the patient. The level of agreements and
discrepancies between motion of the left and right foot, and the
stimulation input, may be used to give an indication of the gait
phase estimation reliability. This can also be transferred to other
movements, including but not limited to stepping, walking, running,
swimming, cycling, rowing.
[0095] Furthermore, the control system may comprise a pre-warning
module, which is configured and arranged to provide a pre-warning
signal indicative of providing an upcoming stimulation event.
[0096] Regulating gait to a predefined reference interferes with
voluntary motion of the patient. In particular, voluntary motion of
the patient may have a large effect on the movement, as the
patients voluntary control may modulate muscle activation. The
movement pattern may therefore differ from comparable to a healthy
subject, to impaired or reduced despite identical stimulation. The
pre-warning signal may help the patient to adjust voluntary control
to the respective movement planed, thus a regular movement may be
performed. The pre-warning signal may include but is not limited to
a sound signal, vibration, light signal, smell, taste, pain,
temperature (warm, cold), humid signal, draught or the like.
[0097] In particular, the pre-warning signal may act in a sub-motor
threshold region at which a sensation is evoked, but not a motor
response.
[0098] In the following it is identified which control output
parameters exist and their effects on the afferent nerves, as well
as their end effects on muscle activation is described. Based on
this, we select which output parameters will be controlled by the
control system.
BRIEF DESCRIPTION OF THE FIGURES
[0099] Further details and advantages of the present invention
shall now be disclosed in connection with the drawings.
[0100] It is shown in
[0101] FIG. 1 a schematic, very simplified representation of a
stimulation pulse delivered by a system according to the present
invention;
[0102] FIG. 2A, B the necessary current and necessary charge to
trigger an action potential in a nerve fiber as a function of the
pulse width (using a square pulse);
[0103] FIG. 3 a table specifying the fiber types, diameter, and
function;
[0104] FIG. 4 the relationship between response delay and
inter-muscle response delays;
[0105] FIG. 5 a table specifying the intended movement and the
involved agonist muscle and the involved antagonist muscle;
[0106] FIG. 6 functional muscle blocks (FMB) and custom muscle
blocks (CMB);
[0107] FIG. 7 a general layout of a control system for a movement
reconstruction and/or restoration system for a patient according to
the present invention; and
[0108] FIG. 8 a schematic drawing of the movement model generation
module.
DETAILED DESCRIPTION
[0109] Note that in the following we primarily refer to CNS/EES
stimulation. The one skilled in the art may transfer the
stimulation parameters to PNS/FES stimulation.
[0110] The control system may provide stimulation data for movement
reconstruction and/or restoration for stimulation of afferent nerve
fibers using electrical current pulses. Given this starting point,
the following stimulation parameters may be identified:
[0111] Electrode configuration (which electrodes to use,
polarity)
[0112] Stimulation (Pulse) amplitude
[0113] Stimulation (Pulse) width
[0114] Stimulation (Pulse) frequency
[0115] FIG. 1 illustrates a schematic, very simplified
representation of the stimulation pulse, which illustrates the
pulse amplitude, pulse width, and pulse frequency. Each stimulation
pulse is followed by a neutralization pulse or a neutralization
period (not depicted) to remove the electric charge from the tissue
in order to avoid tissue damage.
[0116] The effects of each of the stimulation parameters are
described below.
[0117] Electrode configuration: Stimulating a specific muscle group
requires applying a specific electrical field at a specific
location on the spinal cord. Therefore, the electrical stimulation
may be delivered to the spinal cord by a lead with multiple
electrodes. The location, shape, and direction of the electrical
field that is produced may be changed by choosing a different
electrode configuration (which electrodes are used, with which
polarity and potential) that is used to deliver the current. Hence,
the electrode configuration may determine to which spinal roots the
stimulation is delivered, and therefore which subsequent muscles or
muscle groups activity will be reinforced.
[0118] Pulse amplitude and pulse width: In FIG. 2A and FIG. 2B the
necessary current and necessary charge to trigger an action
potential in a nerve fiber are shown as a function of the pulse
width (using a square pulse) (cf Merrill D R. et al., Electrical
Stimulation of excitable tissue: design of efficacious and safe
protocols, J Neurosci methods 141(2):171-98 (2005)). FIG. 2A and
FIG. 2B also show the rheobase current I.sub.rh, which is the
current that is required when using infinitely long pulse widths,
and the chronaxie time t.sub.c, which is the required pulse width
at a current of 2I.sub.rh.
[0119] Although larger currents may be required at smaller pulse
widths, the total required charge may decrease with decreasing
pulse width, see FIG. 2B. Hence shorter pulses with higher current
amplitudes may be energetically beneficial.
[0120] For smaller diameter nerves, the current-pulse width curve
of FIG. 2A shifts, as smaller diameter fibers may require higher
currents. Hence, a higher current may activate more nerve fibers,
as also smaller diameter nerve fibers may be activated (until
saturation). However, also cross-talk is increased as also more
neurons from neighboring roots may be activated. Fortunately, the
afferent fibers involved in motor control (fiber types Ia and Ib)
may be all relatively large (12-20 .mu.m), while the fibers
involved in touch, temperature, and pain feedback (which should not
be triggered) may be relatively small (0.5-12 .mu.m), as depicted
in FIG. 3. Hence, with increasing pulse width and/or current
amplitude, the type Ia and Ib fibers may be the first to be
recruited. This may enable recruiting (most of) the relevant fibers
while keeping cross-talk and patient discomfort to a minimum.
[0121] Pulse frequency: The pulse frequency may determine the
frequency of the action potentials generated in the afferent
nerves, assuming sufficient charge is delivered each pulse to
trigger the action potentials. As no new action potential can occur
in a nerve during the refractory period, the frequency of the
triggered action potentials will saturate at high pulse
frequencies. This saturation point is generally at around 200 Hz
for afferent fibers (Miller J P. et al., Parameters of Spinal Cord
Stimulation and Their Role in Electrical Charge Delivery: A Review.
Neuromodulation: Technology at the Neural Interface 19, 373-384,
(2016)). However, stimulation at frequencies above the saturation
point may still be beneficial, as by increasing frequency the total
charge delivered per unit time (i.e. charge per second) can be
increased without changing current amplitude or pulse width (Miller
J P. et al., Parameters of Spinal Cord Stimulation and Their Role
in Electrical Charge Delivery: A Review. Neuromodulation:
Technology at the Neural Interface 19, 373-384, (2016)).
[0122] Pulse positioning: Many tasks, including walking, require
simultaneous activation of multiple muscle groups. Hence, to
support these tasks, multiple muscle groups may need to be
stimulated simultaneously, each requiring a specific electrical
field and pulse frequency. When applied simultaneously, these
different electrical fields may interact with each other,
potentially leading to unintended and uncontrolled effects.
Therefore, to avoid this situation, care should be taken that
according to the stimulation data, individual stimulation pulses
and their neutralization periods targeting different muscle groups
are not applied simultaneously. This may not be considered a
stimulation parameter but does identify a required system feature:
a pulse positioning algorithm (PPA).
[0123] The previous section describes the effect of the stimulation
parameters on triggering action potentials in afferent nerve
fibers. Although triggering these action potentials is an essential
step in the therapy, in the end the stimulation according to the
stimulation data should enable or support the patient in performing
specific lower body motions, which may require the activation of
specific muscles or muscle groups. The effect of the triggered
action potentials in afferent nerve fibers on muscle activation may
be filtered inside the spinal cord through spinal reflex circuits
and modulated through the voluntary control of the patient. Hence,
the effect of the stimulation parameters on muscle activation may
be not perfectly clear and may be affected by intra- and
inter-patient variations. The following aspects may be of relevance
here:
[0124] Different patients may have different levels of voluntary
control over their lower body, depending on the type and severity
of their SCI lesion level and state of (spontaneous) recovery.
[0125] Stimulation of afferent nerve fibers may assist or enable
activation of the corresponding muscles but may not necessarily
enforce motion. The patient may modulate the activation (e.g. make
a large or small step without changing the stimulation), or even
resist motion of the leg completely. This may vary per patient and
may change with increasing recovery.
[0126] Conjecture: Because the spinal cord floats in the
cerebrospinal fluid, the distance between the spinal cord and the
lead electrodes may vary (mostly as a function of the Patient's
posture: prone--large distance, supine--small distance). Another
hypothesis may be that due to posture changes, the layer thickness
of low conductive epidural fat between the lead electrodes and the
dura/cerebrospinal fluid a changing, leading to an impedance change
as seen by the electrodes, and resulting in an altered
current/voltage delivered stimulation by the electronics. As a
result, the effect of the applied stimulation (including muscle
onset and saturation) may also vary with the patient's posture.
Although this conjecture is not proven, patients may successfully
make use of the described effects to modulate the stimulation
intensity by varying their posture: bending forward reduces the
intensity, bending backward increases it.
[0127] Pulse frequencies between 40 and 120 Hz may mostly being
used, although it may theoretically be possible to stimulate up to
500 Hz as this may have benefits for selectivity in muscle
activation and improved voluntary control of the patient.
[0128] It may be possible that general increasing the pulse
amplitude may not lead to increased recruitment of muscle fibers
(with corresponding increased cross-talk), and that increasing the
stimulation frequency may lead to increased muscle activation
without affecting cross-talk. However, increasing the stimulation
frequency may reduce the intensity of natural proprioception and
result in a decreased feeling in the leg of the patient. This is
probably due to the collision of natural sensory inputs with
antidromic action potentials generated by the electrical
stimulation. At high frequency (above 100 Hz), patients may even
report a complete loss of sensation of the leg and "feel like
walking with their legs being absent". This is a non-comfortable
situation requiring the patient to make a leap of faith at each
single step, believing that the leg that he/she does not feel
anymore will support him/her during the next stance phase.
Adjusting the balance between stimulation amplitude and frequency
may therefore be necessary to find the optimal compromise between
cross-talk limitation and loss of sensation. Simulations suggest
that a possible workaround may be to shift the stimulation domain
to lower amplitudes and even higher frequency, such that with a
minimal number of stimulated fibers the same amount of activity is
triggered in the spinal cord. Such hypothesis requires validation
via additional clinical data. Finally, it may also be identified
that different patients require different stimulation, i.e. that
the optimal frequency and amplitude settings may vary highly
between patients. Hence, the relation between stimulation amplitude
and frequency on muscle activation may be still for a large part
unclear. Moreover, the optimal stimulation settings may vary during
the day, the assistive device that is used (crutches, walker,
etc.), over time with improved recovery, and with the goal of the
training or activity.
[0129] Timing: apart from applying the correct electrical field at
the right location on the spinal cord, they also may need to be
applied at the correct moments in time and correctly sequenced. The
relevant timing aspects that are identified are listed below.
[0130] There is a delay from stimulation on the spinal cord to
muscle activation (typical values in the order of 0-30 ms depending
on the muscle, see FIG. 4, LVLat=left vastus lateralis, RVLat=right
vastus lateralis, Lll=left iliopsoas, Rll=right iliopsoas, LRF=left
rectus femoris, RRF=right rectus femoris, LST=left semitendinosus,
RST=right semidentinosus, LTA=left tibialis anterior, RTA=right
tibialis anterior, LMG=left medial gastrocnemius, RMG=right medial
gastrocnemius, LSol=left soleus, RSol=right soleus, LFHL=left
flexor halluces longus, RFHL=right flexor halluces longus).
[0131] While EES enables patients to perform motions, the patient
may need to be able to predict when the stimulation will occur in
order to make the best use of the stimulation. Likewise,
suppressing motion while stimulation is provided also requires that
the patient knows when to expect the stimulation. Hence,
predictability of the stimulation timing is essential.
[0132] When the stimulation is not synchronized to the patient's
(intended) motion, the patient may not be able to perform a proper
movement. Here, this may mean that the stimulation needs to be
predictable by the patient, as the patient needs to synchronize to
the stimulation.
[0133] The duration of the stimulation for leg swing during walking
may need to be finely tuned. For some patients, increasing the
duration of this stimulation by 100 ms made the patient jump
instead of performing a proper step.
[0134] 20 ms may be a sufficient resolution for tuning the
stimulation timings (i.e. the on/off times of the stimulation for a
specific muscle group may not need to be controlled at a precision
below 20 ms). Given current data availability, controlling the
timings at resolutions below 20 ms may not seem to improve the
effectiveness of the stimulation.
[0135] Based on the previous sections, the stimulation parameters
may be selected. This may determine the control output space that
is used, and therefore the complexity of the control problem and
the potential effectiveness of the control system.
[0136] First it is discussed which parameter spaces can be reduced
or eliminated. The remaining control output space is summarized
below.
[0137] Electrode configuration: Walking, as well as other movements
of the lower extremities, may be composed of well-coordinated
flexion and extension of lower body joints by contraction of
agonist muscles and relaxation of antagonist muscles. The specific
set of agonist and antagonist muscles for joint specific flexion
and extension may be grouped, and as the number of joints is
limited, this means that only a small discrete set of muscle groups
may be needed to be stimulated. For each joint flexion and
extension, the optimal electrode configuration will be created for
activation of the agonist muscles while avoiding activation of the
antagonist muscles (as well as avoiding activation of muscles on
the contralateral side). This may be done in a procedure called the
functional mapping. We define the Functional Muscle Blocks (FMB),
as the resulting stimulation configurations for each specific
muscle group. At least 12 specific FMBs have been identified for
using the control system, these are listed in FIG. 5 with their
corresponding agonists and antagonists.
[0138] As knee flexion and hip extension both involve the
semitendinosus, it is physically not possible to target knee
flexion and hip extension separately. Therefore, FIG. 5 does not
include knee flexion (this could be considered redundant to hip
extension).
[0139] Next to the 12 FMB listed in FIG. 5, it is also envisioned
that the trainer/therapist/physiotherapist may create Custom Muscle
Blocks (CMB). Creating CMB may be useful in case the
trainer/therapist/physiotherapist wants to apply stimulation that
does not specifically target any of the 12 muscle groups targeted
by the FMB, or in case the trainer/therapist/physiotherapist wants
to use a variant of one of the 12 FMB in a specific Task.
[0140] Hence, by limiting the electrode configurations to the
discrete set of FMB and CMB (versus an infinite number of possible
electrode configurations), the control problem complexity may be
reduced considerably without significantly affecting the potential
effectiveness of the control system. Stimulation for a Task is then
reduced to stimulation of (a subset of) the predefined FMB and CMB,
see FIG. 6. In this example, the Right Trunk Stability is used in
both Task 1 and Task 2.
[0141] The functional mapping procedure may require measuring the
response of each of the muscles listed in FIG. 5 with EMG sensors.
Due to the large number of muscles, this requires attaching many
EMG sensors to the patient (which is time consuming) and processing
a large amount of data. Moreover, as motion of the patient may
induce signal artifacts, the functional mapping may be best
performed while the patient is not moving. For these reasons, the
functional mapping procedure may be performed in a separate session
using a Space Time Programmer (STP) for e.g. programming space and
time of the stimulation, and not e.g. adaptively within the control
system. Hence, the configuration of FMB and CMB may be considered
as a given to the control system.
[0142] Pulse width: From the viewpoint of triggering action
potentials in afferent nerve fibers, the parameters pulse width and
pulse amplitude may be tightly linked and may together determine
which afferent nerve fibers are recruited. Increasing the pulse
width may allow to reduce the amplitudes and decreasing the pulse
width may allow reducing energy consumption (as the total required
charge for triggering an action potential decreases with decreasing
pulse width, see FIG. 2B and stimulating more FMB simultaneously or
at higher frequencies. However, from a control perspective the two
parameters may be (almost) redundant, as increasing either
parameter may lead to the recruitment of more afferent nerve fibers
over a larger area.
[0143] Pulse widths below chronaxie time t.sub.c may quickly
require high currents (and thus high voltages), which is difficult
to produce and may lead to patient discomfort. Beyond t.sub.c, the
strength-duration curve of FIG. 2A is almost flat, so increasing
pulse width beyond t.sub.c has little effect on the required
amplitudes while it increases total power consumption. Also
considering that having a fixed pulse width simplifies the pulse
positioning, the pulse width is chosen to be fixed (at a value near
chronaxie time t.sub.c such that both energy consumption and
required current amplitudes remain low, where t.sub.c.apprxeq.200
.mu.s for afferent dorsal root nerve fibers in humans). This
reduces the complexity of the control problem by reducing the
number of output parameters.
[0144] This may leave the following stimulation parameters to be
controlled over time by the control system:
[0145] Which FMBs to stimulate
[0146] Stimulation amplitude per FMB
[0147] Stimulation frequency per FMB
[0148] The pulse positioning may be considered a lower level
problem and may therefore be not a direct output of the control
system (system feature). The pulse positioning will be performed by
the IPG.
[0149] Although combining amplitude and frequency to a single
`intensity` parameter has been considered, doing so is not
envisioned for the control system, as these parameters may have
very different effects. On triggering action potentials in afferent
nerve fibers, the amplitude and frequency may be independent
parameters: the amplitude determines in which afferent nerve fibers
action potentials are triggered, the frequency determines the rate
at which they are triggered. Hence, in principle the amplitude
determines which muscle fibers are activated, the frequency
determines how hard, although it is unclear if the independence of
the two parameters also holds for muscle activation due to the
signal processing that occurs in the spinal cord. Moreover, it may
be apparent that for some patients changing the amplitude gives the
best results, while for other patients the frequency may be the
more useful parameter.
[0150] As the precise relation between frequency and amplitude is
not known in the clinical context it may not be recommended to
combine frequency and amplitude to single parameter. Hence, the
stimulation frequency and amplitude may be controlled independently
from each other.
[0151] In the following the principle of the sensor and the
stimulation system (e.g. IPG) of the present invention are
described in greater detail.
[0152] Sensors: Battery powered, body worn sensors (directly or
indirectly), collecting motion data, and sending it to the
controller. Its intended use is to capture body motion
parameters.
[0153] Stimulation system, here IPG: Implantable Pulse Generator. A
battery powered device that generates the electrical stimulation,
subcutaneously implanted. Its intended use is to deliver electrical
stimulation to the lead based on command received from the motion
controller.
[0154] The control system may further comprise or may be linked to
a programmer.
[0155] Programmer: The programmer, or also called the clinician
programmer, can be used to receive inter alia stimulation
parameter, patient data, physiological data, training data etc.
[0156] It may comprise a Space Time Programmer (STP) for e.g.
programming space and time of the stimulation, a Physiotherapist
Programmer (PTP) for e.g. allowing the physiotherapist adjustment
to the stimulation, and a Patient Programmer (PP) for e.g. allowing
the patient to select a specific stimulation program.
[0157] The STP, Physiotherapist Programmer (PTP), and Patient
Programmer (PP) can be embodied as applications installed on a
mobile device that communicate with the control system. They are
used by the treating physician (TP), a physiotherapist (PT), or the
patient to provide inputs to the control system, e.g., selecting,
starting, and stopping a task or configuring stimulation
parameters.
[0158] The programmer may allow adjusting the stimulation
parameters of a task, while the task is running. This enables the
user to tune the stimulation without having to start and stop the
task, which would be very cumbersome at the start of the
rehabilitation training, when all stimulation partitures are
developed and tuned.
[0159] Generally speaking, the programmer may have the following
structure:
[0160] In a first embodiment, the programmer can be embodied such
that it is possible to receive inter alia but not limited to
stimulation parameters, patient data and the like, check and/or
reprogram the stimulation data and send it back to e.g. the control
system.
[0161] The programmer is in this first embodiment capable to
receive data from the implanted (part of the) system (e.g. the
motion controller), display data, receive input from the user and
then send it back to the control system. In other words: The
programmer can receive, process and re-send the data.
[0162] In a second embodiment, the programmer may receive data from
a remote database. The database may be e.g. linked with the
stimulation system via a separate interface, which is configured
for data transfer from the system to the database only.
[0163] The programmer is in this second embodiment capable to
receive data from the remote database, display data, receive input
from the user and then send it to the motion controller. In other
words: The programmer is only in connection with the control system
for sending data, it does not receive data from the control system
or any implanted system parts.
[0164] FIG. 7 shows a general layout of a control system for a
movement reconstruction
[0165] and/or restoration system for a patient according to the
present invention.
[0166] The present embodiment comprises a control system 10 for a
gait reconstruction system for a patient according to the present
invention.
[0167] The control system 10 comprises an input module 12.
[0168] The control system 10 also comprises a preprocessing module
14.
[0169] In this embodiment, the preprocessing module 14 is a Kalman
filter.
[0170] However, also other preprocessing modules 14 are generally
possible.
[0171] Furthermore, the control system 10 comprises in the shown
embodiment a movement model generation module 16.
[0172] Additionally, the control system 10 comprises an analysis
module 18.
[0173] In this embodiment, the analysis model 18 is a real-time
analysis module 18.
[0174] The control system 10 comprises also a filter 20.
[0175] In an alternative embodiment, the control system 10
comprises more than one filter 20.
[0176] The filter 20 in the present embodiment is a particle filter
20.
[0177] However, the filter 20 could generally also be a Kalman
filter, a histogram filter, a stochastic filter or the like.
[0178] The control system 10 is connected to a sensor 24 and an
implantable pulse generator (IPG) 22.
[0179] The input module 12 is connected to the preprocessing module
14.
[0180] The preprocessing module 14 is connected to the movement
model generation module 16.
[0181] Further, the preprocessing module 14 is connected to the
real-time analysis module 18.
[0182] The movement model generation module 16 is connected to the
real-time analysis module 18.
[0183] The real-time analysis module 18 is connected to the filter
20.
[0184] The connection between the input module 12 and the
preprocessing module 14, the preprocessing module 14 and the
movement model generation module 16, the preprocessing module 14
and the real-time analysis module 18, the movement model generation
module 16 and the real-time analysis module 18, and the real-time
analysis module 18 and the filter 20 is in the shown embodiment is
a direct connection.
[0185] However, also an indirect connection (i.e. with another
component of the control system 10 in between) would be generally
possible.
[0186] The connection between the input module 12 and the
preprocessing module 14, the preprocessing module 14 and the
movement model generation module 16, the preprocessing module 14
and the real-time analysis module 18, the movement model generation
module 16 and the real-time analysis module 18, and the real-time
analysis module 18 and the filter 20 is established in the shown
embodiment via a wireless network WSN.
[0187] However, also a cable-bound connection would be generally
possible.
[0188] The control system 10 is connected to the sensor 24 via a
wireless network WSN.
[0189] However, also a cable-bound connection would be generally
possible.
[0190] The control system 10 is connected to the IPG 22 via a
wireless connection TEL.
[0191] However, also a cable-bound connection would be generally
possible.
[0192] The input module 12 receives sensor input data from one or
more sensors 24 or one or more sensor networks directly or
indirectly attached to a patient.
[0193] The input module 12 receives sensor input data, the sensor
input data describing a phase of a movement.
[0194] The sensor data are transferred from the input module 12 to
the preprocessing module 14 and there preprocessed.
[0195] In other words, the preprocessing module 14 preprocesses the
sensor input data received from the input module 12.
[0196] In this embodiment, the preprocessing module 14 is a Kalman
filter.
[0197] The Kalman filter is the estimation part of the optimal
control solution to the linear-quadratic-Gaussian control
problem.
[0198] The Kalman filter is an algorithm that uses a series of
measurements observed over time, containing statistical noise and
other inaccuracies, and produces estimates of unknown variables
that tend to be more accurate than those based on a single
measurement alone, by estimating a joint probability distribution
over the variables for each timeframe.
[0199] However, also other embodiments of a preprocessing module 14
could be generally possible.
[0200] By means of the movement model generation module 16 a
movement model m' is generated based on the preprocessed sensor
data from the preprocessing module 14.
[0201] The real-time analysis module 18 receives and processes data
provided by the preprocessing module 14 and the movement model
generation module 16.
[0202] The real-time analysis module 18 uses the movement model m'
created by the movement model generation module 16 in real-time in
order to deduce the probability density function of the movement
phase.
[0203] Then, the data are transferred from the real-time analysis
module 18 to the filter 20.
[0204] The filter 20 filters the data provided by the real-time
analysis module 18.
[0205] The filter 20 represents the phase probability density
function as a set of particles.
[0206] Not shown in FIG. 7 is that for gait, the gait speed of the
movement phase is retrieved as a single normal distribution to be
used in the filter 20.
[0207] By means of the filter 20, estimation of previous time steps
is taken into account to get a movement phase, e.g. a gait phase,
that is consistent in time.
[0208] Thus, the filter 20 allows to establish a best estimate for
the true value of the movement model m from an incomplete,
potentially noisy set of observations on that movement model m and
the corresponding movement.
[0209] The movement model m is translated into stimulation data
d.
[0210] In this embodiment, the control system 10 programs the IPG
22 to deliver stimulation according to the stimulation data d.
[0211] In this embodiment, the control system 10 is body worn.
However, also non-body worn alternatives are possible.
[0212] Not shown in FIG. 7 is that the control system 10 extracts
at least one base frequency out of sensor input data.
[0213] In particular, the base frequency is indicative of a cadence
of the movement.
[0214] The cadence of the left foot should be equal to the cadence
of the right foot and the cadence of the provided stimulation, and
the left foot and right foot should be (roughly) in anti-phase.
[0215] Not shown in FIG. 7 is that the control system 10 could also
be used for a reconstruction and/or restoration system for cyclic
movements other than gait, including but not limited to stepping,
swimming, rowing or cycling.
[0216] Not shown in FIG. 7 is that the control system 10 may
comprise a pre-warning module, which is configured and arranged to
provide a pre-warning signal indicative of providing an upcoming
stimulation event.
[0217] FIG. 8 shows a schematic drawing of the movement model
generation module.
[0218] The movement model generation module 16 of the control
system 10 disclosed in FIG. 7 comprises a sensor data buffer
161.
[0219] In other words, the control system 10 may comprise at least
one sensor data buffer 161.
[0220] The movement model generation module 16 further comprises an
offline expert system 162.
[0221] The movement model generation module 16 may comprise further
offline expert systems 162.
[0222] In other words, the control system 10 comprises at least one
offline expert system.
[0223] The movement model generation module 16 further comprises a
movement event library 163.
[0224] In another embodiment, the movement model generation module
16 could also comprise more than one movement event library
163.
[0225] In other words, the control system 10 could comprise at
least one movement event library 163.
[0226] In this embodiment, the movement model generation module 16
further comprises a model training module 164.
[0227] In another embodiment, the movement model generation module
16 could comprise more than just one model training module 164.
[0228] In other words, the control system 10 could comprise at
least one model training module 164.
[0229] The sensor data buffer 161 is connected to the offline
expert system 162.
[0230] The offline expert system 162 is connected to the movement
event library 163.
[0231] The movement event library 163 is connected to the model
training module 164.
[0232] As disclosed in FIG. 7, the movement model generation module
16 of the control system 10 is connected to the preprocessing
module 14 of the control system 10.
[0233] The connections between the sensor data buffer 161 and the
offline expert system 162, the offline expert system 162 and the
movement event library 163, the movement event library 163 and the
model training module 164 is a direct connection.
[0234] However, also an indirect connection (i.e. with another
component of the model training module 16 in between) would be
generally possible.
[0235] The connections between the sensor data buffer 161 and the
offline expert system 162, the offline expert system 162 and the
movement event library 163, the movement event library 163 and the
model training module 164 is a wireless connection WSN.
[0236] However, also a cable-bound connection would be generally
possible.
[0237] The connection between the movement model training module 16
and the preprocessing module 14, as disclosed in FIG. 7, is in this
embodiment a wireless connection WSN.
[0238] However, also a cable-bound connection would be generally
possible.
[0239] Preprocessed sensor data (compare FIG. 7) are transferred
from the preprocessing module 14 to the sensor data buffer 161 of
the movement model generation module 16.
[0240] The sensor data buffer 161 stores preprocessed sensor
data.
[0241] In this embodiment, data are recorded for a complete
rehabilitation session.
[0242] However, shorter periods of data recording could be
generally possible.
[0243] However, data should be recorded for a period of time of
minimum one complete movement, e.g. gait cycle.
[0244] Based on the accumulated sensor data in the sensor data
buffer 161, the offline expert system 162 determines a movement
event library 163 comprising a list of different gait events and
phase offline.
[0245] The offline expert system 162 may allow to use criteria that
could not be used in real-time.
[0246] It could be possible to use the created gait phase offline
at any time.
[0247] Possible gait events in the movement event library 163 could
include but are not limited to initial ground contact, heel strike,
foot flat, loading response, midstance, terminal stance, heel off,
preswing, toe off, initial swing, midswing, terminal swing, and/or
heel strike.
[0248] However, it is possible that there are only two events
(foot-strike and foot-off) for gait/walking.
[0249] Note that the movement event library 163 determined by the
offline system 162 is characterized by showing always the same
value at the same event and being a linear interpolation between
two consecutive events.
[0250] Based on the movement event library 163, the model training
module 164 trains the movement model m', here the gait model m',
using recent preprocessed sensor data in the sensor data buffer 161
to adapt the movement model m' to the particular movement of the
patient.
[0251] In other words, the model training module 164 prepares and
provides the movement model m' on the basis of a fusion of sensor
input data and a previous movement model m'.
[0252] In other words, the movement model generation module 16 is a
learning system.
[0253] In other words, various preprocessed sensor data from the
preprocessing module 14 update the sensor data buffer 161 and as
soon as a whole gait cycle is detected, the past gait event is
determined online by the model training module 164.
[0254] It is possible that the sensor data buffer 161 could
comprise accumulated preprocessed sensor data from one patient,
such as several or multiple recording sessions, and/or from two or
more patients and/or from one or more trainers and/or from one or
more healthy subjects.
[0255] Beyond a movement model m' for gait cycle (i.e. gait model),
the movement model generation module 164 could prepare and provide
the movement model m' on the basis of a fusion of sensor input data
and a previous movement model m' for various movements, including
but not limited to stepping, cycling, swimming, running,
rowing.
[0256] The example control and estimation routines included herein
can be used with various system configurations. The control methods
and routines disclosed herein may be stored as executable
instructions in non-transitory memory and may be carried out by a
control system 10 e.g. as a part of the movement model generation
module 16 in combination with the input module 12, the
preprocessing module 14, the real-time analysis module 18, the
filter 20, and other system hardware. The specific routines
described herein may represent one or more of any number of
processing strategies such as event-driven, interrupt-driven,
multi-tasking, multi-threading, and the like. As such, various
actions, operations, and/or functions illustrated may be performed
in the sequence illustrated, in parallel, or in some cases omitted.
Likewise, the order of processing is not necessarily required to
achieve the features and advantages of the example embodiments
described herein but is provided for ease of illustration and
description. One or more of the illustrated actions, operations
and/or functions may be repeatedly performed depending on the
particular strategy being used. Further, the described actions,
operations and/or functions may graphically represent code to be
programmed into non-transitory memory of a computer readable
storage medium in the control system 10, where the described
actions are carried out by executing the instructions in a control
system 10 including the various hardware components.
REFERENCES
[0257] 10 control system [0258] 12 input module [0259] 14
preprocessing module [0260] 16 movement model generation module
[0261] 18 (real-time) analysis module [0262] 20 filter [0263] 22
implantable pulse generator (IPG) [0264] 24 sensor [0265] 161
sensor data buffer [0266] 162 offline expert system [0267] 163
(movement) event library [0268] 164 model training module [0269] d
stimulation data [0270] m movement model [0271] m' movement model
[0272] CMB custom muscle blocks [0273] FMB functional muscle block
[0274] IPG implantable pulse generator [0275] TEL connection,
telemetry line [0276] WSN wireless network [0277] LVLat left vastus
lateralis [0278] RVLat right vastus lateralis [0279] Lll left
iliopsoas [0280] Rll right iliopsoas [0281] LRF left rectus femoris
[0282] RRF right rectus femoris [0283] LST left semitendinosus
[0284] RST right semidentinosus [0285] LTA left tibialis anterior
[0286] RTA right tibialis anterior [0287] LMG left medial
gastrocnemius [0288] RMG right medial gastrocnemius [0289] LSol
left soleus [0290] RSol right soleus [0291] LFHL left flexor
halluces longus [0292] RFHL right flexor halluces longus
* * * * *